app.py

import streamlit as st
import tempfile
import base64
import os
import nltk
nltk.download('punkt')
from src.utils.ingest_text import create_vector_database
from src.utils.ingest_image import extract_and_store_images
from src.utils.text_qa import qa_bot
from src.utils.image_qa import query_and_print_results
import nest_asyncio
from langchain.memory import ConversationBufferWindowMemory
from langchain_community.chat_message_histories import StreamlitChatMessageHistory
from dotenv import load_dotenv



nest_asyncio.apply()
load_dotenv()

st.set_page_config(layout='wide', page_title="InsightFusion Chat")

memory_storage = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferWindowMemory(
    memory_key="chat_history",
    human_prefix="User",
    chat_memory=memory_storage,
    k=3
)

image_bg = r"data/pexels-andreea-ch-371539-1166644.jpg"

def add_bg_from_local(image_file):
    with open(image_file, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
    st.markdown(f"""<style>.stApp {{
        background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
        background-size: cover
    }}</style>""", unsafe_allow_html=True)

add_bg_from_local(image_bg)

st.markdown("""
    <svg width="600" height="100">
        <text x="50%" y="50%" font-family="San serif" font-size="42px" fill="Black" text-anchor="middle" stroke="white"
         stroke-width="0.3" stroke-linejoin="round">InsightFusion Chat
        </text>
    </svg>
""", unsafe_allow_html=True)

def get_answer(query, chain):
    try:
        response = chain.invoke(query)
        return response['result']
    except Exception as e:
        st.error(f"Error in get_answer: {e}")
        return None

uploaded_file = st.file_uploader("File upload", type="pdf")
path = None

# Handle uploaded file
if uploaded_file is not None:
    temp_file_path = os.path.join("temp", uploaded_file.name)
    os.makedirs("temp", exist_ok=True)
    with open(temp_file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    path = os.path.abspath(temp_file_path)
    st.write(f"File saved to: {path}")
    st.write("Document uploaded successfully!")

# Option to use a predefined demo PDF from pdf_resource folder
st.markdown("### Or use a demo file:")
if st.button("Use Demo PDF"):
    demo_file_path = os.path.join("pdf_resource", "sample.pdf")  # Replace with actual demo file name
    if os.path.exists(demo_file_path):
        path = os.path.abspath(demo_file_path)
        st.write(f"Using demo file: {path}")
        st.success("Demo file loaded successfully!")

        with st.spinner("Processing demo file..."):
            try:
                client = create_vector_database(path)
                image_vdb = extract_and_store_images(path)
                chain = qa_bot(client)
                st.session_state['chain'] = chain
                st.session_state['image_vdb'] = image_vdb
                st.success("Demo file processing complete.")
            except Exception as e:
                st.error(f"Error processing demo PDF: {e}")
    else:
        st.error("Demo file not found. Make sure 'pdf_resource/sample.pdf' exists.")

# Process uploaded file on button click
if st.button("Start Processing"):
    if path is not None:
        with st.spinner("Processing"):
            try:
                client = create_vector_database(path)
                image_vdb = extract_and_store_images(path)
                chain = qa_bot(client)
                st.session_state['chain'] = chain
                st.session_state['image_vdb'] = image_vdb
                st.success("Processing complete.")
            except Exception as e:
                st.error(f"Error during processing: {e}")
    else:
        st.error("Please upload a file or use the demo before starting processing.")

# Custom input background
st.markdown("""
    <style> 
    .stChatInputContainer > div {
        background-color: #000000;
    }
    </style>
""", unsafe_allow_html=True)

# Chat logic
if user_input := st.chat_input("User Input"):
    if 'chain' in st.session_state and 'image_vdb' in st.session_state:
        chain = st.session_state['chain']
        image_vdb = st.session_state['image_vdb']

        with st.chat_message("user"):
            st.markdown(user_input)
        
        with st.spinner("Generating Response..."):
            response = get_answer(user_input, chain)
            if response:
                with st.chat_message("assistant"):
                    st.markdown(response)
                
                memory.save_context(
                    {"input": user_input},
                    {"output": response}
                )

                st.session_state.messages.append({"role": "user", "content": user_input})
                st.session_state.messages.append({"role": "assistant", "content": response})

                try:
                    query_and_print_results(image_vdb, user_input)
                except Exception as e:
                    st.error(f"Error querying image database: {e}")
            else:
                st.error("Failed to generate response.")
    else:
        st.error("Please start processing before entering user input.")

# Initialize message state
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display message history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# Display chat memory history (LangChain)
for i, msg in enumerate(memory_storage.messages):
    name = "user" if i % 2 == 0 else "assistant"
    st.chat_message(name).markdown(msg.content)